Common best proximity points: global minimal solutions

Let us suppose that A and B are nonempty subsets of a metric space. Let S:A?B and T:A?B be nonself-mappings. Considering the fact S and T are nonself-mappings, it is feasible that the equations Sx=x and Tx=x have no common solution, designated as a common fixed point of the mappings S and T. Eventually, when the equations have no common solution, one contemplates to figure out an element x that is in close proximity to Sx and Tx in the sense that d(x,Sx) and d(x,Tx) are minimum. In fact, common best proximity point theorems scrutinize the existence of such optimal approximate solutions, known as common best proximity points, to the equations Sx=x and Tx=x in the event that the equations have no common solution. Further, one can perceive that the real-valued functions x?d(x,Sx) and x?d(x,Tx) estimate the magnitude of the error involved for any common approximate solution of the equations Sx=x and Tx=x. In light of the fact that the distance between x and Sx, and the distance between x and Tx are at least the distance between A and B for all x in A, a common best proximity point theorem ascertains global minimum of both functions x?d(x,Sx) and x?d(x,Tx) by limiting a common approximate solution of the equations Sx=x and Tx=x to fulfil the requirement that d(x,Sx)=d(A,B) and d(x,Tx)=d(A,B). This article discusses a common best proximity point theorem for a pair of nonself-mappings, one of which dominates the other proximally, thereby yielding common optimal approximate solutions of some fixed point equations when there is no common solution.     

Best proximity points: global minimization of multivalued non-self mappings

In this article, we give a best proximity point theorem for generalized contractions in metric spaces with appropriate geometric property. We also, give an example which ensures that our result cannot be obtained from a similar result due to Amini-Harandi (Best proximity points for proximal generalized contractions in metric spaces. Optim Lett, 2012). Moreover, we prove a best proximity point theorem for multivalued non-self mappings which generalizes the Mizoguchi and Takahashi’s fixed point theorem for multivalued mappings.     

Best proximity points: approximation and optimization

A best proximity point theorem explores the existence of an optimal approximate solution, known as a best proximity point, to the equations of the form Tx = x where T is a non-self mapping. The purpose of this article is to establish some best proximity point theorems for non-self non-expansive mappings, non-self Kannan- type mappings and non-self Chatterjea-type mappings, thereby producing optimal approximate solutions to some fixed point equations. Also, algorithms for determining such optimal approximate solutions are furnished in some cases.     

Best proximity points of non-self mappings

Let A,B be nonempty subsets of a Banach space X and let T:A→B be a non-self mapping. Under appropriate conditions, we study the existence of solutions for the minimization problem min _x∈A ∥x?Tx∥.     

Global optimal approximate solutions

Given non-void subsets A and B of a metric space and a non-self mapping T:A? B{T:A\longrightarrow B}, the equation T x = x does not necessarily possess a solution. Eventually, it is speculated to find an optimal approximate solution. In other words, if T x = x has no solution, one seeks an element x at which d(x, T x), a gauge for the error involved for an approximate solution, attains its minimum. Indeed, a best proximity point theorem is concerned with the determination of an element x, called a best proximity point of the mapping T, for which d(x, T x) assumes the least possible value d(A, B). By virtue of the fact that d(x, T x) ≥ d(A, B) for all x in A, a best proximity point minimizes the real valued function x? d(x, T x){x\longrightarrow d(x, T\,x)} globally and absolutely, and therefore a best proximity in essence serves as an ideal optimal approximate solution of the equation T x = x. The aim of this article is to establish a best proximity point theorem for generalized contractions, thereby producing optimal approximate solutions of certain fixed point equations. In addition to exploring the existence of a best proximity point for generalized contractions, an iterative algorithm is also presented to determine such an optimal approximate solution. Further, the best proximity point theorem obtained in this paper generalizes the well-known Banach’s contraction principle.     

Best proximity points for asymptotic cyclic contraction mappings

In this paper, we prove the existence and convergence of best proximity points for asymptotic cyclic contractions in metric spaces with the property UC, as well as for asymptotic proximal pointwise contractions in uniformly convex Banach spaces. Moreover, we consider a generalized cyclic contraction mapping and prove the existence of best proximity points for such a mapping in Banach spaces which do not necessarily satisfy the geometric property UC.     

Best proximity points for cyclic Meir–Keeler contractions

We introduce a notion of cyclic Meir–Keeler contractions and prove a theorem which assures the existence and uniqueness of a best proximity point for cyclic Meir–Keeler contractions. This theorem is a generalization of a recent result due to Eldred and Veeramani.     

Common best proximity points: global minimization of multi-objective functions

Given non-empty subsets A and B of a metric space, let ${S{:}A{\longrightarrow} B}$ and ${T {:}A{\longrightarrow} B}$ be non-self mappings. Due to the fact that S and T are non-self mappings, the equations Sx = x and Tx = x are likely to have no common solution, known as a common fixed point of the mappings S and T. Consequently, when there is no common solution, it is speculated to determine an element x that is in close proximity to Sx and Tx in the sense that d(x, Sx) and d(x, Tx) are minimum. As a matter of fact, common best proximity point theorems inspect the existence of such optimal approximate solutions, called common best proximity points, to the equations Sx = x and Tx = x in the case that there is no common solution. It is highlighted that the real valued functions ${x{\longrightarrow}d(x, Sx)}$ and ${x{\longrightarrow}d(x, Tx)}$ assess the degree of the error involved for any common approximate solution of the equations Sx = x and Tx = x. Considering the fact that, given any element x in A, the distance between x and Sx, and the distance between x and Tx are at least d(A, B), a common best proximity point theorem affirms global minimum of both functions ${x{\longrightarrow}d(x, Sx)}$ and ${x{\longrightarrow}d(x, Tx)}$ by imposing a common approximate solution of the equations Sx = x and Tx = x to satisfy the constraint that d(x, Sx) = d(x, Tx) = d(A, B). The purpose of this article is to derive a common best proximity point theorem for proximally commuting non-self mappings, thereby producing common optimal approximate solutions of certain simultaneous fixed point equations in the event there is no common solution.     

Best proximity points: approximation and optimization in partially ordered metric spaces

The purpose of this article is to provide the existence of a unique best proximity point for non-self-mappings by using altering distance function in the setting of partially ordered set which is endowed with a metric. Further, our result provides an extension of a result due to Harjani and Sadarangani to the case of non-self-mappings.     

Best proximity points for proximal generalized contractions in metric spaces

In this paper, we give best proximity point theorem for non-self proximal generalized contractions. Moreover, an algorithm is exhibited to determine such an optimal approximate solution designed as a best proximity point. An example is also given to support our main results.     

Searching approximate global optimal Heilbronn configurations of nine points in the unit square via GPGPU computing

In this paper we present a method of applying the GPGPU technology to compute the approximate optimal solution to the Heilbronn problem for nine points in the unit square, namely, points \(P_1,P_2,\ldots ,P_9\) in \([0,1]\times [0,1]\) so that the minimal area of triangles \(P_iP_jP_k\,(1\le i<j<k\le 9)\) gets the maximal value \(h_9(\Box )\). We construct nine rectangles with edge length 1 / 80 in the unit square which contain all optimal Heilbronn configurations up to possible rotation and reflection, and prove that \(\frac{9\sqrt{65}-55}{320}=0.054875999\cdots<h_9(\Box )<0.054878314\), the lower bound here comes from Comellas and Yebra’s paper.     

Best proximity points,cyclic Kannan maps and geodesic metric spaces

We introduce the concept of cyclic Kannan orbital C-nonexpansive mappings and obtain the existence of a best proximity point on a pair of bounded, closed and convex subsets of a strictly convex metric space by using the geometric notion of seminormal structure. We also study the structure of minimal sets for cyclic Kannan C-nonexpansive mappings and show that results similar to the celebrated Goebel– Karlovitz lemma for nonexpansive self-mappings can be obtained for cyclic Kannan C-nonexpansive mappings.     

Best proximity point theorems

Let us assume that A and B are non-empty subsets of a metric space. In view of the fact that a non-self mapping T:A?B does not necessarily have a fixed point, it is of considerable significance to explore the existence of an element x that is as close to Tx as possible. In other words, when the fixed point equation Tx=x has no solution, then it is attempted to determine an approximate solution x such that the error d(x,Tx) is minimum. Indeed, best proximity point theorems investigate the existence of such optimal approximate solutions, known as best proximity points, of the fixed point equation Tx=x when there is no solution. Because d(x,Tx) is at least d(A,B), a best proximity point theorem ascertains an absolute minimum of the error d(x,Tx) by stipulating an approximate solution x of the fixed point equation Tx=x to satisfy the condition that d(x,Tx)=d(A,B). This article establishes best proximity point theorems for proximal contractions, thereby extending Banach’s contraction principle to the case of non-self mappings.     

Extremal points and optimal solutions for general capacity problems

This paper studies the infinite dimensional linear programming problems in the integration type. The variable is taken in the space of bounded regular Borel measures on compact Hausdorff spaces. It will find an optimal measure for a constrained optimization problem, namely a capacity problem. Relations between extremal points of the feasible region and optimal solutions of the optimization problem are investigated. The necessary/sufficient conditions for a measure to be optimal are established. The algorithm for optimal solution of the general capacity problem onX = Y = [0, 1] is formulated.     

Existence theorems for usual and approximate solutions of optimal control problems

The theory of measurable set-valued mappings allows us to study some problems of optimal control in the framework of minimization of convex functionals and thus to obtain existence theorems. When the functionals are nonconvex, we obtain the existence theorems for control problems which are weakly perturbed from the initial one. In this regard, we specify some theorems of nonconvex optimization.     

Some common best proximity points for proximity commuting mappings

In this paper, we prove new common best proximity point theorems for a proximity commuting mapping in a complete metric space. Our results generalized a recent result of Sadiq Basha [Common best proximity points: global minimization of multi-objective functions, J. Glob. Optim., (2011)] and some results in the literature.     

A discrete filled function algorithm for approximate global solutions of max-cut problems

A discrete filled function algorithm is proposed for approximate global solutions of max-cut problems. A new discrete filled function is defined for max-cut problems and the properties of the filled function are studied. Unlike general filled function methods, using the characteristic of max-cut problems, the parameters in proposed filled function need not be adjusted. This greatly increases the efficiency of the filled function method. By combining a procedure that randomly generates initial points for minimization of the filled function, the proposed algorithm can greatly reduce the calculation cost and be applied to large scale max-cut problems. Numerical results on different sizes and densities test problems indicate that the proposed algorithm is efficient and stable to get approximate global solutions of max-cut problems.     

Best Lp approximate solutions of nonlinear integrodifferential equations

In this paper best L_p approximate solutions are shown to exist for a wide class of integrodifferential equations. Using approximation theory techniques, a local existence theorem for solutions is established, and the convergence of the best approximate solutions to a solution is shown.